News • Study on devices and implants

AI could improve speech recognition in hearing aids

In noisy environments, it is difficult for hearing aid or hearing implant users to understand their conversational partner because current audio processors still have difficulty focusing on specific sound sources. In a feasibility study, researchers from the Hearing Research Laboratory at the University of Bern and the Inselspital are now suggesting that artificial intelligence (AI) could solve this problem.

golden ear-shaped art installation on concrete wall

Image source: Unsplash/Jaee Kim

The study has been published in the journal Hearing Research.

Hearing aids or hearing implants are currently not very good at selectively filtering specific speech from many sound sources for the wearer – a natural ability of the human brain and sense of hearing known in audiology as the “cocktail party effect”. Accordingly, it is difficult for hearing aid users to follow a conversation in a noisy environment. Researchers at the Hearing Research Laboratory of the ARTORG Center, University of Bern, and Inselspital have now devised an unusual approach to improve hearing aids in this respect: virtual auxiliary microphones whose signals are calculated by an AI.

portrait of tim fischer
Dr Tim Fischer

The more microphones are available and the more widely they are distributed, the better a hearing aid can focus on sound from a particular direction. Most hearing aids have two microphones close together due to lack of space. In the first part of the study, the Hearing Research Laboratory (HRL) determined that the optimal microphone location (for better focusing) is in the middle of the forehead – though this is a very impractical location. “We wanted to get around this problem by adding a virtual microphone to the audio processor using artificial intelligence,” said Tim Fischer, a postdoctoral researcher at HRL, explaining this unconventional approach.

For the study setup, ARTORG Center engineers used the “Bern Cocktail Party Dataset”, a collection of a variety of noise scenarios with multiple sound sources from multi-microphone recordings of hearing aid or cochlear implant users. Using 65 hours of audio recordings (more than 78,000 audio files), they trained a neural network to refine a commonly used directionality algorithm (beamformer). For improved speech understanding the deep learning approach calculated additional virtual microphone signals from the audio data mixture. 20 subjects tested the AI-enhanced hearing in a subjective hearing test accompanied by objective measurements. Particularly in cocktail party settings, the virtually sampled microphone signals significantly improved the speech quality. Hearing aid and cochlear implant users could therefore benefit from the presented approach, especially in noisy environments.

Placement of the 16 microphones used for cocktail party scenario recordings
Placement of the 16 microphones used for 'cocktail party scenario' recordings

Image source: Fischer et al., Hearing Research 2021 (CC BY-NC-ND 4.0)

"I think that artificial intelligence represents an important contribution to the next generation of hearing prostheses, as it has great potential for improving speech understanding, especially in difficult listening situations," says Marco Caversaccio, Chief Physician and ENT Department Head. As auditory assistive technologies and implants are a major focus of research at the Inselspital, important data-based foundations are being laid here for further development that should bring the natural hearing experience closer. The novel approaches will directly benefit patients within the framework of translational studies.

Although within this study the virtually added microphones significantly improved the quality of speech understanding with hearing aids, further studies still need to overcome some technical hurdles before the methodology can be used in hearing aids or cochlear implant audio processors. This includes, for example, a stable functioning directional understanding even in reverberant environments. 

Source: Inselspital, University Hospital Bern


Read all latest stories

Related articles


News • What's your sound barrier?

Misophonia: new insights into intolerance of everyday sounds

UK researchers have shown that misophonia, a condition where those affected show a strong negative reaction to everyday sounds, may affect almost one fifth of the general population.


News • Study on chatbot reliability and accuracy

Can you count on ChatGPT for cancer information?

Chatbots become popular resources for cancer information - but are their results accurate? Researchers evaluated the reliability of ChatGPT’s cancer information.


News • Artificial intelligence in radiology

AI accurately identifies normal and abnormal chest X-rays

An artificial intelligence tool can accurately identify normal and abnormal chest X-rays in a clinical setting, according to a new study. The tool identified abnormal X-rays with a 99.1% sensitivity.

Related products

3DQuorum SmartSlices

Artificial Intelligence

Hologic · 3DQuorum SmartSlices

Hologic, Inc.
Advanced intelligent Clear-IQ Engine for MR

Artificial Intelligence

Canon · Advanced intelligent Clear-IQ Engine for MR

Canon Medical Systems Europe B.V.
AI-Rad Companion

Artificial Intelligence

Siemens Healthineers · AI-Rad Companion

Siemens Healthcare GmbH
Aquilion Exceed LB

Oncology CT

Canon · Aquilion Exceed LB

Canon Medical Systems Europe B.V.
Aquilion Lightning

20 to 64 Slices

Canon · Aquilion Lightning

Canon Medical Systems Europe B.V.
Subscribe to Newsletter